The achievement gap between public and private school students is well-documented in the literature on education in India, but there has not been sufficient focus on the dynamics of this gap over time. Analysing Young Lives Survey data on student test scores from Andhra Pradesh and Telangana, this article suggests that very little convergence is expected, as students make their way through middle and high school.
The academic achievement gap between students attending public and private schools in India has been widely studied in the recent development literature (see for example, Azam et al. 2016, Chudgar and Quin 2012, French and Kingdon 2010, Kingdon 1996, Muralidharan and Kremer 2008, Muralidharan and Sundararaman 2015, Singh 2014, Singh 2015). Most studies so far have found evidence of private school students outperforming public school students on standardised tests, although there is considerable variation in the magnitude of the estimated gaps with some studies documenting large gaps in achievement while others finding evidence of only modest gaps. In McDonough et al. (2021), we employ two empirical methods that are common in the literature for measuring movement through the distribution of income/wages over time (Bhattacharya and Mazumder 2011, Buchinsky and Hunt 1999), to estimate the likelihood that public and private school students will transition in a directional sense (upward, downward, etc.) through the distribution of academic achievement between five and 12 years of age.
Patterns rather than levels
Understanding the dynamics of the achievement gap between public and private school students is of fundamental importance to researchers and policymakers for at least two reasons. First, solely evaluating the achievement gap between public and private school students in levels and not taking into account the pattern of students’ relative achievement over time, can result in the stakeholders drawing incomplete conclusions regarding the extent of the gap.
Second, the documented public-private test score gap is unlikely to be robust to various ‘scale transformations’. In other words, the magnitude of the gap and how it evolves can vary solely on the basis of how the underlying test scores are scaled (Bond and Lang 2013, Jacob and Rothstein 2016). However, and as noted by McDonough (2015), distributional mobility measures can address this concern as long as the rank order of students within the test score distribution is unchanged.
Data and methodology
We use data from the Young Lives Survey (YLS), focussing on school-aged children in the states of Andhra Pradesh and Telangana, during 2002-2014. The YLS surveyed two cohorts of children: a younger and an older cohort.1 We focus on the 2,011 children who belong to the younger cohort (aged six to 21 months, surveyed in the first wave of YLS in 2002). For children belonging to the younger cohort, YLS conducted math and Peabody Picture Vocabulary Test (PPVT)2 in all rounds of the survey except the first round, since the children were too young (six to 21 months old) to respond to any academic test.
Using these data, we estimate ‘conditional transition probabilities’ (CTP) and ‘directional rank mobilities’ (DRM) for public and private school students over two time periods: 2006/2007 to 2009/2010 (when the children were between five and eight years of age, that is, early childhood) and 2009/2010 to 2013/2014 (when the children were between eight and 11 years of age, that is, preadolescence). CTP capture the likelihood of a child belonging to a given quartile in the initial period ending up in the same or different quartile3 in the final period. DRM indicates the probability that a child’s percentile rank in the achievement distribution is a certain amount higher (or lower) in the final period than their rank in the achievement distribution in the initial period.4
Our results indicate that during early childhood, private school students are not significantly more (less) upwardly (downwardly) mobile than public school students in both math as well as PPVT. During pre-adolescence, however, we find clear evidence of private school students being at an advantage relative to the public school students in terms of having more upward mobility and less downward mobility5. Specifically, during preadolescence, we find that compared to private school students, public school students exhibit significant downward mobility in both subjects. For example, private school students are 10 to 29 percentage points and 9 to 19 percentage points more likely to move upwards in the math and PPVT test score distribution respectively, relative to their counterparts in public schools. Moreover, public school students, compared to private school students, seem to have a 10 to 28 percentage point and a 9 to 19 percentage point chance of moving downward across the math and PPVT test score distribution respectively. Our results are robust to the inclusion of various controls (for example, parental perception about child ability, wealth, caste, etc.) that may determine parental decisions regarding which type of school (public or private) a child should be enrolled in (Wadhwa 2018), and may also be correlated with the child’s achievement.
Our results present a rather alarming picture of the disparity in academic performance between public and private school students. Coupled with the existence of a level gap in test scores, the divergent movements through the distribution of academic achievement observed in the data suggest that the documented gap in test scores between public and private school students is likely to persist as students make their way through middle and high school. Our results emphasise the need for policymakers to think about smart and effective policies that can be implemented before the onset of preadolescence to promote higher upward mobility and lower downward mobility, for public school students.
I4I is now on Telegram. Please click here (@Ideas4India) to subscribe to our channel for quick updates on our content.
The data were collected over five waves with the first round of data collection taking place in 2002, the second in late 2006/early 2007, the third in late 2009/early 2010, the fourth in late 2013/early 2014, and the final round in late 2016/early 2017.
he PPVT score is a widely used test to measure verbal ability and general cognitive development among children.
We define this as staying probability in case the child stays in the same quartile, and downward/upward transition probability in case of movement across quartiles.
Different degrees of movement can also be selected – for example, movements of more than 5, 10 or 20 percentiles can be calculated.
As they are more likely to move upward and less likely to move downward as compared to their counterparts in public schools.
- Azam, Mehtabul, Geeta Kingdon and Kin Bing Wu (2016), “Impact of Private Secondary Schooling on Cognitive Skills: Evidence from India”, Education Economics, 24: 465–80. Available here.
- Bhattacharya, Debopam and Bhashkar Mazumder (2011), “A Nonparametric Analysis of Black-White Differences in Intergenerational Income Mobility in the United States”, Quantitative Economics, 2(3): 335-79.
- Bond, Timothy N and Kevin Lang (2013), "The evolution of the Black-White test score gap in Grades K–3: The fragility of results", The Review of Economics and Statistics, 95(5): 1468-1479. Available here.
- Buchinsky, Moshe and Jennifer Hunt (1999), “Wage Mobility in the United States” The Review of Economics and Statistics, 81(3): 351-68. Available here.
- Chudgar, Amita and Elizabeth Quin (2012), “Relationship between Private Schooling and Achievement: Results from Rural and Urban India”, Economics of Education Review, 31(4): 376-90.
- French, R and G Kingdon (2010), ‘The Relative Effectiveness of Private and Government Schools in Rural India: Evidence from ASER Data’, University College London Department of Quantitative Social Science, Working Paper 10-03.
- Jacob, Brian and Jesse Rothstein (2016), "The measurement of student ability in modern assessment systems", Journal of Economic Perspectives, 30(3): 85-108.
- Kingdon, Geeta (1996), “The Quality and Efficiency of Public and Private Education: A Case Study of Urban India”, Oxford Bulletin of Economics and Statistics, 58(1): 57-82.
- McDonough, Ian K (2015), "Dynamics of the black–white gap in academic achievement", Economics of Education Review, 47: 17-33.
- McDonough, Ian K, Punarjit Roychowdhury and Gaurav Dhamija (2021), “Measuring the Dynamics of the Achievement Gap Between Public and Private School Students During Early Life in India”, Journal of Labor Research, 42: 78-122.
- Muralidharan, K and M Kremer (2008), ‘Public and Private Schools in Rural India’, in R Chakrabarti and P Petersen (eds.), School choice international: Exploring public–private partnerships.
- Muralidharan, Karthik and Venkatesh Sundararaman (2015), “The Aggregate Effect of School Choice: Evidence from a Two-Stage Experiment in India”, The Quarterly Journal of Economics, 130(3): 1011-66.
- Singh, Abhijeet (2014), “Test Score Gaps between Private and Government Sector Students at School Entry Age in India”, Oxford Review of Education, 30: 30-49. Available here.
- Singh, Abhijeet (2015), “Private School Effects in Urban and Rural India: Panel Estimates at Primary and Secondary School Ages”, Journal of Development Economics, 113: 16-32. Available here.
- Wadhwa, W (2018), ‘Equity in learning?’, Annual Status of Education Report (Rural) 2018, Pratham, New Delhi, India. Available here.